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AGING, October 2015, Vol. 7 No 10
Research Paper Excess body weight increases the burden of age‐associated chronic diseases and their associated health care expenditures 1,2 Vincenzo Atella , Joanna Kopinska1, Gerardo Medea3, Federico Belotti1, Valeria Tosti4, Andrea 1 Piano Mortari , Claudio Cricelli, MD3, and Luigi Fontana4,5,6 1 Department of Economics and Finance, University of Rome "Tor Vergata”, Rome, Italy; 2 Center for Health Policy, Stanford University, Stanford, CA 94305, USA; 3 Italian College of General Practitioners (SIMG), Florence, Italy; 4 Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; 5 Department of Clinical and Experimental Sciences, Brescia University, Italy; 6 CEINGE Biotecnologie Avanzate, Napoli, Italy. Key words: cost analysis, body mass index, disease burden, cardiovascular disease, diabetes, hypertension, obesity Received: 09/18/15; Accepted: 10/06/15; Published: 10/29/15 Correspondence to: Luigi Fontana, MD/PhD; E‐mail:
[email protected] Copyright: Atella et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Abstract: Aging and excessive adiposity are both associated with an increased risk of developing multiple chronic diseases, which drive ever increasing health costs. The main aim of this study was to determine the net (non‐estimated) health costs of excessive adiposity and associated age‐related chronic diseases. We used a prevalence‐based approach that combines accurate data from the Health Search CSD‐LPD, an observational dataset with patient records collected by Italian general practitioners and up‐to‐date health care expenditures data from the SiSSI Project. In this very large study, 557,145 men and women older than 18 years were observed at different points in time between 2004 and 2010. The proportion of younger and older adults reporting no chronic disease decreased with increasing BMI. After adjustment for age, sex, geographic residence, and GPs heterogeneity, a strong J‐shaped association was found between BMI and total health care costs, more pronounced in middle‐aged and older adults. Relative to normal weight, in the 45‐64 age group, the per‐capita total cost was 10% higher in overweight individuals, and 27 to 68% greater in patients with obesity and very severe obesity, respectively. The association between BMI and diabetes, hypertension and cardiovascular disease largely explained these elevated costs.
INTRODUCTION The current obesity epidemic in an increasingly aging population presents health, long-term care, and welfare systems with new challenges [1]. Increased consumption of energy-dense, nutrient-poor foods and a sedentary lifestyle have led to this sharp and unprecedented rise in the rates of overweight and obesity, which has been estimated to increase both direct and indirect health care costs due to lost productivity [2, 3]. Aging and excess adiposity are both well-established risk factors for the development and progression of several chronic diseases, including type 2 diabetes, hypertension, dyslipidaemia, cardiovascular
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disease (CVD), osteoarthritis, depression and certain prevalent cancers (i.e. colon, breast, and prostate) [4]. The diagnosis and treatment of these common and preventable chronic diseases places a significant burden on National Health Service budgets. However, little is known about the true (non-estimated) impact of body mass index (BMI) on the inpatient and outpatient health care costs for these adiposity-associated chronic diseases. This study has several advantages over most existing studies. The majority of studies published so far have estimated the total health care costs of obesity by modelling group and individual level data with various degree of representativeness at national level, often relying on self-reported clinical information and
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proxies of medical expenditures [5-11]. In this study, we used a patient-based approach combining health care cost data and accurate anthropometric and clinical informations collected by general practitioners in a large representative longitudinal sample of more than 550 thousand Italian men and women, homogeneously distributed across all Italian regions. Moreover, Italy is an ideal setting for this type of analysis, since its National Health Service provides universal and substantially free health care access to all citizens, with 87% of medical services publicly financed [12], avoiding problems of patient selection associated with insurance based health care systems. Finally, by modelling this data with a seemingly unrelated regression equation (SURE) statistical method, we were able to disentangle the direct and indirect (i.e. obesity-associated diseases) impact of BMI on health care system spending.
RESULTS Sample descriptive statistics Table 1 summarizes the breakdown of the variables according to BMI classes, showing large differences in demographic and clinical parameters such as age and prevalence of comorbidities. Hypertension and type 2 diabetes were the most common BMI-associated health conditions and their prevalence shows a strong increase with increasing BMI (p=0.0001 for all BMI categories with respect to normal weight individuals). In addition, the prevalence of dyslipidaemia, CVD and arthrosis were higher in individuals with overweight and obesity than in normal weight individuals in both younger (55 yrs) patients (p=0.0001 for all BMI categories with respect to normal weight individuals). There was a clear negative association between BMI and the proportion of individuals with no chronic disease (p=0.0001 for all BMI categories with respect to normal weight individuals). In contrast, the proportion of individuals affected by 2 or more chronic diseases increased sharply with raising BMI. BMI and healthcare costs Table 2 and Table 3 present the coefficient estimates of indirect (i.e. obesity-associated diseases), direct and differential effects of BMI on outpatient and total health care costs for each age group. The tables report marginal effects for each BMI category within each age specific subsample, as well as the percentage differences of the marginal effect estimate with respect to the annual average expenditure of normo-weight individuals. As shown in Figure 1, after adjusting for age, sex, geographic residence, and GPs heterogeneity, there was a
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J-shaped association between BMI and overall (direct and indirect) total health care expenditure, which was stronger among middle-aged and elderly individuals. Total health care expenditures among the middle-aged (45-64 yrs old) individuals with overweight, obesity, severe obesity and very severe obesity were 10%, 27%, 52% and 68% higher, respectively, than among those with BMI of 18.5 to 24.9 (p=0.0001) (third panel of Table 3). In absolute terms, outpatient costs were more strongly related to BMI among individuals aged 45 to 64 years. The annual mean costs among the overweight, obesity, severe obesity and very severe obesity patients were 76, 159, 237, and 310 euro higher, respectively, than in normo-weight individuals, which translates in a cost increase of about 18%, 38%, 57% and 75% for each BMI category, respectively (third panel of Table 2). In contrast, total costs were more strongly related to BMI among individuals aged 65+ years. The annual differential mean costs among the patients with overweight, obesity, severe obesity and very severe obesity were 75, 302, 719, and 790 euro higher, respectively, than in normal weight individuals (fourth panel of Table 3). Total overall (direct and indirect) costs were also significantly higher in underweight individuals than in normal weight individuals in all subsamples (Table 3). In particular, annual mean total costs among all underweight individuals were 138 euro higher than in the normo-weight subjects, which translates in a cost increase of 13% (first panel of Table 3). Indirect and direct costs The share of indirect costs within overall outpatient costs was the largest in overweight and obese men and women aged 45-64 years (Table 2). Moreover, the indirect costs of the underweight subjects were lower than those of the normo-weight individuals for each age group, and this differential was more pronounced in the elderly, amounting to 11% (Table 2). In terms of total outpatient and inpatient health expenditure, direct costs were negative in the overweight and obesity groups, suggesting that after correcting for BMI-related pathologies, these patients on average had lower health care expenditures than normo-weight individuals. This direct cost differential turned positive for the category with severe and very severe obesity, and was particularly pronounced in the elderly (Table 3). Moreover, the total indirect costs in the underweight individuals were significantly lower than in the normoweight subjects, and this differential was particularly high in the elderly (fourth panel of Table 3). Finally, the total direct costs in the underweight men and women were substantially higher than in the elderly normoweight individuals.
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Figure 1. Total (a) and outpatient (b) health care expenditure. Decomposition of differences in health care expenditure (direct, indirect and overall costs) by age group and BMI category compared to normal‐weight individuals (euro per year) for outpatient (b) and total (a) health expenditure. Note: ‐ Indirect marginal effects for each BMI category were computed as the sum of nonlinear combinations of parameters estimated within each pathology‐specific equation with the respective pathology‐specific parameter estimated within the health expenditure equation. ‐ Direct marginal effects for each BMI category were obtained as relative parameter estimates from the health expenditure equation. ‐ Overall marginal effects for each BMI category was computed as the sum of the respective direct and indirect marginal effects.
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884 AGING, October 2015, Vol. 7 No.10
Table 1. Demographic and clinical characteristics of individuals 18 years or older, by BMI categories Under weight (15.0018.49)
BMI
Normal weight (18.5024.99)
Over Weight (25.0029.99)
Obesity class I (30.0034.99)
Obesity class II (35.0039.99)
Obesity class III (≥ 40.00)
72032 1103594 997802 390109 105731 35943 Sample size 17.5* 22.3 27.2* 32* 36.9* 43.6* Mean BMI (kg/m2) 82.3%* 61.0% 44.8%* 50.2%* 62.4%* 71.7%* Gender (% female) 39* 48 56* 57* 56* 54* Mean age (yrs) Age group (%) 25%* 10% 3%* 2%* 2%* 3%* 18-24 28%* 17% 8%* 7%* 7%* 8%* 25-34 19%* 20% 15%* 14%* 14%* 15%* 35-44 9%* 17% 19%* 19%* 20%* 21%* 45-54 6%* 13% 21%* 24%* 25%* 26%* 55-64 6%* 11% 19%* 21%* 21%* 19%* 65-74 8%* 10% 14%* 14%* 12%* 8%* 75+ Comorbidities (%) Age group under 55 0.4%* 1.1% 3.5%* 7.3%* 12.1%* 16.7%* Diabetes 1.9%* 6.4% 17.4%* 27.8%* 36.5%* 42.5%* Hypertension 1.9%* 5.4% 11.6%* 13.3%* 12.0%* 9.4%* Dyslipidemia 0.2%* 0.6% 1.5%* 2.0%* 2.2%* 2.0%* CVD 4.7%* 4.0% 3.9%* 4.6%* 5.6%* 6.7%* Depression 0.5%* 0.6% 0.6% 0.7% 0.7% 0.7% Cancers 0.9%* 1.2% 2.0%* 2.7%* 3.7%* 4.9%* Arthrosis Age group over 55 5.6%* 12.5% 19.9%* 28.0%* 35.0%* 38.7%* Diabetes 38.9%* 49.4% 61.9%* 72.0%* 78.9%* 82.8%* Hypertension 18.8%* 26.9% 30.3%* 29.8%* 28.0%* 23.1%* Dyslipidemia 9.9%* 11.4% 13.9%* 14.7%* 14.6%* 12.3%* CVD 10.4%* 8.0% 6.7%* 7.2%* 8.1% 8.3% Depression 6.5%* 6.0% 5.7%* 5.3%* 4.7%* 4.1%* Cancer 6.5%* 8.0% 10.2%* 13.1%* 15.9%* 18.0%* Arthrosis No. of comorbidities Age group under 55 90.5%* 83.5% 68.5%* 58.1%* 50.3%* 45.4%* 0 8.7%* 14.0% 24.0%* 29.0%* 31.8%* 33.3%* 1 0.8%* 2.2% 6.1%* 9.9%* 13.4%* 15.5%* 2 0.1%* 0.3% 1.4%* 3.1%* 4.4%* 5.8%* >=3 Age group over 55 37.6%* 28.0% 19.3%* 13.4%* 9.9%* 8.5%* 0 37.0% 36.6% 35.4%* 33.1%* 31.0%* 31.1%* 1 18.4%* 23.6% 28.0%* 30.8%* 32.3%* 33.8%* 2 7.1%* 11.8% 17.4%* 22.8%* 26.8%* 26.6%* >=3 44%* 40% 34%* 31%* 31%* 28%* Smokers (%) 8124 138211 135669 54825 15007 5013 (subsample in which smoking information was available) * p
